Fingerprint classification through self-organizing feature maps modified to treat uncertainties

1996-10-01
In this paper, a neural network structure based on Self Organizing Feature Maps (SOM) is proposed for fingerprint classification. In order to be able to deal with fingerprint images having distorted regions, the SOM learning and classification algorithms are modified. For this purpose, the concept of ''certainty'' is introduced and used in the modified algorithms. This fingerprint classifier together with a fingerprint identifier, constitute subsystems of an automated fingerprint identification system named HALafis.(1) Our results show that a network that is trained with a sufficiently large and representative set of samples can be used as an indexing mechanism for a fingerprint database, so that it does not need to be retained for each fingerprint added to the database.
PROCEEDINGS OF THE IEEE

Suggestions

MRF Based Image Segmentation Augmented with Domain Specific Information
Karadag, Ozge Oztimur; Yarman Vural, Fatoş Tunay (2013-09-13)
A Markov Random Field based image segmentation system which combines top-down and bottom-up segmentation approaches is proposed in this study. The system is especially proposed for applications where no labeled training set is available, but some priori general information referred as domain specific information about the dataset is available. Domain specific information is received from a domain expert and formalized by a mathematical representation. The type of information and its representation depends o...
A new scheme for off-line handwrittten connected digit recognition
Arica, N; Yarman Vural, Fatoş Tunay (1998-04-23)
In this study, we introduce a new scheme for off-line handwritten connected digit string recognition problem, which uses a sequence of segmentation and recognition algorithms. The proposed system assumes no constraint in writing style, size or variations.
A Shadow based trainable method for building detection in satellite images
Dikmen, Mehmet; Halıcı, Uğur; Department of Geodetic and Geographical Information Technologies (2014)
The purpose of this thesis is to develop a supervised building detection and extraction algorithm with a shadow based learning method for high-resolution satellite images. First, shadow segments are identified on an over-segmented image, and then neighboring shadow segments are merged by assuming that they are cast by a single building. Next, these shadow regions are used to detect the candidate regions where buildings most likely occur. Together with this information, distance to shadows towards illuminati...
Face classification with support vector machine
Kepenekci, B; Akar, Gözde (2004-04-30)
A new approach to feature based frontal face recognition with Gabor wavelets and support vector machines is presented in this paper. The feature points are automatically extracted using the local characteristics of each individual face. A kernel that computes the similarity between two feature vectors, is used to map the face features to a space with higher dimension. To find the identity of a test face, the possible labels of each feature vector of that face is found with support vector machines, then the ...
A Feature Extraction Method for Marble Tile Classification
DEVİREN, Murat; M KORAY, Balcı; Leloğlu, Uğur Murat; SEVERCAN, Mete (2000-03-03)
This study focuses on a feature extraction algorithm for classification of marble tiles. The color content and vein distribution are considered to be the main criteria for classification. A color segmentation algorithm is used for detection of veins. The shape analysis of the veins are done by utilizing the distance image.
Citation Formats
U. Halıcı, “Fingerprint classification through self-organizing feature maps modified to treat uncertainties,” PROCEEDINGS OF THE IEEE, pp. 1497–1512, 1996, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47668.